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Compound optimal designs for percentile estimation in dose-response models with restricted design intervals

Compound optimal designs for percentile estimation in dose-response models with restricted design intervals
Compound optimal designs for percentile estimation in dose-response models with restricted design intervals
In dose-response studies, the dose range is often restricted due to ethics concerns over drug toxicity and/or efficacy, particularly when human subjects are involved. We present locally optimal designs for the estimation of several percentiles simultaneously on restricted as well as unrestricted design intervals. Our results hold true for most of the commonly applied link functions with respect to the model under consideration.
dose-response model, link function, percentile estimation, compound optimal design, a-optimality
0378-3758
3838-3847
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Dette, Holger
8c7b1c2e-3adc-45df-acfc-9e76509a228e
Zhu, Wei
83068cea-979a-4276-ae7c-c989b252979b
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Dette, Holger
8c7b1c2e-3adc-45df-acfc-9e76509a228e
Zhu, Wei
83068cea-979a-4276-ae7c-c989b252979b

Biedermann, Stefanie, Dette, Holger and Zhu, Wei (2007) Compound optimal designs for percentile estimation in dose-response models with restricted design intervals. Journal of Statistical Planning and Inference, 137 (12), 3838-3847. (doi:10.1016/j.jspi.2007.04.003).

Record type: Article

Abstract

In dose-response studies, the dose range is often restricted due to ethics concerns over drug toxicity and/or efficacy, particularly when human subjects are involved. We present locally optimal designs for the estimation of several percentiles simultaneously on restricted as well as unrestricted design intervals. Our results hold true for most of the commonly applied link functions with respect to the model under consideration.

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Published date: 2007
Keywords: dose-response model, link function, percentile estimation, compound optimal design, a-optimality
Organisations: Statistics

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Local EPrints ID: 58642
URI: http://eprints.soton.ac.uk/id/eprint/58642
ISSN: 0378-3758
PURE UUID: 4ad13ff8-dd47-4e5f-af79-3e85a38973b8
ORCID for Stefanie Biedermann: ORCID iD orcid.org/0000-0001-8900-8268

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Date deposited: 19 Aug 2008
Last modified: 09 Jan 2022 03:22

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Contributors

Author: Holger Dette
Author: Wei Zhu

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